Real-time transient stability prediction of power systems based on the energy of signals obtained from PMUs

Sevda Jafarzadeh, V. M.Istemihan Genc*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

In this study, a novel methodology based on signal processing and machine learning approaches is proposed for real-time transient stability prediction (TSP) in power systems where the signals obtained from PMUs are utilized. The proposed method for TSP takes the computed energy of PMU signals in a window of measurements, as an input to a classifier to predict the stability of the system. Several types of classifiers, which are multi-layered perceptrons (MLPs), decision trees, and naïve Bayes classifiers, are employed. Two alternative approaches of choosing the window of measurements used for TSP are proposed, where an MLP-based fault detection process is also proposed to form the proper window of measurements. One approach is to use a fixed window of only post-fault measurements, whereas the other approach is to use an expanding window of measurements covering pre-fault, fault-on and post-fault stages. Utilization of the energy concept in TSP gives the flexibility to process signals in different sizes while providing predictions robust to measurement noises and missing data. It also makes feature selection methods directly applicable, making the TSP possible with less PMUs. The proposed methods are applied to two different test systems and a large-scale model of the Turkish power system.

Original languageEnglish
Article number107005
JournalElectric Power Systems Research
Volume192
DOIs
Publication statusPublished - Mar 2021

Bibliographical note

Publisher Copyright:
© 2020

Funding

This work is supported by The Scientific and Technological Research Council of Turkey (TUBITAK) project no. 118E184. The authors would like to thank TUBITAK for supporting the project and to the Turkish Electricity Transmission Company TEIAS for providing the model of the Turkish power system.

FundersFunder number
TUBITAK118E184
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

    Keywords

    • Feature extraction
    • Machine learning
    • Transient stability prediction

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